True): when the frame width cannot fit within the screen, the output will be broken into multiple pages. • width: width of display screen in characters, used to determine the width of lines when expand_repr multiple lines, ‘max_columns‘ is still respected, but the output will wrap-around across multiple "pages" if it’s width exceeds ‘display.width‘. display.float_format : [default: None] [currently: None]: multiple lines, max_columns is still respected, but the output will wrap-around across multiple “pages” if it’s width exceeds display.width. display.float_format [[default: None] [currently: None]: callable]

argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) s’) display.expand_frame_repr allows for the the representation of dataframes to stretch across pages, wrapped over the full column vs row-wise. In [29]: df=pd.DataFrame(np.random.randn(5,10)) In [30]: multiple lines, max_columns is still respected, but the output will wrap-around across multiple “pages” if it’s width exceeds display.width. display.float_format None The callable should accept a floating

argument that is passed to the underlying parser library. You can use this to read non-ascii encoded web pages (GH7323). • read_excel now supports reading from URLs in the same way that read_csv does. (GH6809) s’) display.expand_frame_repr allows for the the representation of dataframes to stretch across pages, wrapped over the full column vs row-wise. In [29]: df=pd.DataFrame(np.random.randn(5,10)) In [30]: multiple lines, max_columns is still respected, but the output will wrap-around across multiple “pages” if it’s width exceeds display.width. display.float_format None The callable should accept a floating

values to a selection based on loc/iloc. A full overview about indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. In [1]: import pandas as pd This tutorial uses the titanic data set, stored

values to a selection based on loc/iloc. A full overview about indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. 60 Chapter 1. Getting started pandas: powerful Python data analysis toolkit

values to a selection based on loc/iloc. A full overview about indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. In [1]: import pandas as pd This tutorial uses the Titanic data set, stored

values to a selection based on loc/iloc. A full overview about indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. 1.4. Tutorials 37 pandas: powerful Python data analysis toolkit, Release

values to a selection based on loc/iloc. A full overview about indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. 62 Chapter 2. Getting started pandas: powerful Python data analysis toolkit

values to a selection based on loc/iloc. A full overview of indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. In [1]: import pandas as pd This tutorial uses the Titanic data set, stored

values to a selection based on loc/iloc. A full overview of indexing is provided in the user guide pages on indexing and selecting data. In [1]: import pandas as pd In [2]: import matplotlib.pyplot as pandas is a Matplotlib object. A full overview of plotting in pandas is provided in the visualization pages. In [1]: import pandas as pd For this tutorial, air quality data about ??2 is used, made available a variable A full description on the split-apply-combine approach is provided in the user guide pages about groupby operations. In [1]: import pandas as pd This tutorial uses the Titanic data set, stored